Application of Novel Big Data Intelligent Corrosion Assessment Approach in Corrosion‐resistant Low‐alloy Steel Development

Low-alloy steel's corrosion resistance relies on elements like Cu, Cr, Ni, Si, P, and Mo, which enhance rust layer density via substitution, self-oxidation, or acid-salt formation. Optimizing alloy design requires understanding these mechanisms. Welding, crucial for cost reduction, introduces structural heterogeneity in the heat-affected zones (HAZs), worsening corrosion. Uniform microstructures (e.g. bainite) improve rust layer stability. Traditional corrosion evaluation (e.g. exposure tests) is slow; machine learning (ANN, SVM, and RF) now enables faster, data-driven predictions. This chapter examines Cr, Mo, and Sn effects in tropical marine environments, analyzes HAZ corrosion, and develops AI-based evaluation models to advance corrosion-resistant steel development.